> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify-poc.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.

# loading_column()

> Load data to embed directly from a column in the source table

Load data to embed directly from a column in the source table. This is the most common loading method for embedding
textual content stored in your database.

## Samples

### Load from a text column

```sql theme={"dark"}
SELECT ai.create_vectorizer(
    'blog_posts'::regclass,
    loading => ai.loading_column('content'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768),
    chunking => ai.chunking_character_text_splitter(512)
);
```

### Load from multiple tables

```sql theme={"dark"}
-- For products table
SELECT ai.create_vectorizer(
    'products'::regclass,
    loading => ai.loading_column('description'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768)
);

-- For reviews table
SELECT ai.create_vectorizer(
    'reviews'::regclass,
    loading => ai.loading_column('review_text'),
    embedding => ai.embedding_openai('text-embedding-3-small', 768)
);
```

## Arguments

| Name          | Type   | Default | Required | Description                                    |
| ------------- | ------ | ------- | -------- | ---------------------------------------------- |
| `column_name` | `TEXT` | -       | ✔        | Name of the column containing the data to load |

## Returns

A JSON configuration object for use in [`create_vectorizer()`][create_vectorizer].

## Related functions

* [`loading_uri()`][loading_uri]: load data from files referenced by URIs
* [`create_vectorizer()`][create_vectorizer]: main function using this configuration

[create_vectorizer]: /api-reference/pgai/vectorizer/create_vectorizer

[loading_uri]: /api-reference/pgai/vectorizer/loading_uri